Estimating the mirror seeing for a large optical telescope with a numerical method
Bibliographic record
Abstract
Abstract It is widely accepted that mirror seeing is caused by turbulent fluctuations in the index of air refraction in the vicinity of a telescope mirror. Computational Fluid Dynamics (CFD) is a useful tool to evaluate the effects of mirror seeing. In this paper, we present a numerical method to estimate the mirror seeing for a large optical telescope (∼ 4 m) in cases of natural convection with the ANSYS ICEPAK software. We get the FWHM of the image for different inclination angles ( i ) of the mirror and different temperature differences (Δ T ) between the mirror and ambient air. Our results show that the mirror seeing depends very weakly on i , which agrees with observational data from the Canada-France-Hawaii Telescope. The numerical model can be used to estimate mirror seeing in the case of natural convection although with some limitations. We can determine Δ T for thermal control of the primary mirror according to the simulation, empirical data and site seeing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".